OpenRaiser/Intern-Atlas huggingface dataset:
CoopScenes: Multi-Scene Infrastructure and Vehicle Data for Advancing Collective Perception in Autonomous Driving
Venue: arXiv cs.RO 2025
Year: 2025

The increasing complexity of urban environments has underscored the potential of effective collective perception systems. To address these challenges, we present the CoopScenes dataset, a large-scale, multi-scene dataset that provides synchronized sensor data from both the ego-vehicle and the supporting infrastructure.The dataset provides 104 minutes of spatially and temporally synchronized data at 10 Hz, resulting in 62,000 frames. It achieves competitive synchronization with a mean deviation of only 2.3 ms. Additionally the dataset includes a novel procedure for precise registration of point cloud data from the ego-vehicle and infrastructure sensors, automated annotation pipelines, and an open-source anonymization pipeline for faces and license plates. Covering nine diverse scenes with 100 maneuvers, the dataset features scenarios such as public transport hubs, city construction sites, and high-speed rural roads across three cities in the Stuttgart region, Germany. The full dataset amounts to 527 GB of data and is provided in the .4mse format, making it easily accessible through our comprehensive development kit. By providing precise, large-scale data, CoopScenes facilitates research in collective perception, real-time sensor registration, and cooperative intelligent systems for urban mobility, including machine learning-based approaches.
StixelNExT: Toward Monocular Low-Weight Perception for Object Segmentation and Free Space Detection
Venue: arXiv cs.CV 2025
Year: 2025

In this work, we present a novel approach for general object segmentation from a monocular image, eliminating the need for manually labeled training data and enabling rapid, straightforward training and adaptation with minimal data. Our model initially learns from LiDAR during the training process, which is subsequently removed from the system, allowing it to function solely on monocular imagery. This study leverages the concept of the Stixel-World to recognize a medium level representation of its surroundings. Our network directly predicts a 2D multi-layer Stixel-World and is capable of recognizing and locating multiple, superimposed objects within an image. Due to the scarcity of comparable works, we have divided the capabilities into modules and present a free space detection in our experiments section. Furthermore, we introduce an improved method for generating Stixels from LiDAR data, which we use as ground truth for our network.
Scenario-Based Field Testing of Drone Missions
Venue: arXiv cs.SE 2025
Year: 2025

Testing and validating Cyber-Physical Systems (CPSs) in the aerospace domain, such as field testing of drone rescue missions, poses challenges due to volatile mission environments, such as weather conditions. While testing processes and methodologies are well established, structured guidance and execution support for field tests are still weak. This paper identifies requirements for field testing of drone missions, and introduces the Field Testing Scenario Management (FiTS) approach for adaptive field testing guidance. FiTS aims to provide sufficient guidance for field testers as a foundation for efficient data collection to facilitate quality assurance and iterative improvement of field tests and CPSs. FiTS shall leverage concepts from scenario-based requirements engineering and Behavior-Driven Development to define structured and reusable test scenarios, with dedicated tasks and responsibilities for role-specific guidance. We evaluate FiTS by (i) applying it to three use cases for a search-and-rescue drone application to demonstrate feasibility and (ii) interviews with experienced drone developers to assess its usefulness and collect further requirements. The study results indicate FiTS to be feasible and useful to facilitate drone field testing and data analysis
Using deep neural networks to detect non-analytically defined expert event labels in canoe sprint force sensor signals
Venue: arXiv cs.CV 2025
Year: 2025

Assessing an athlete's performance in canoe sprint is often established by measuring a variety of kinematic parameters during training sessions. Many of these parameters are related to single or multiple paddle stroke cycles. Determining on- and offset of these cycles in force sensor signals is usually not straightforward and requires human interaction. This paper explores convolutional neural networks (CNNs) and recurrent neural networks (RNNs) in terms of their ability to automatically predict these events. In addition, our work proposes an extension to the recently published SoftED metric for event detection in order to properly assess the model performance on time windows. In our results, an RNN based on bidirectional gated recurrent units (BGRUs) turned out to be the most suitable model for paddle stroke detection.
MITL Model Checking via Generalized Timed Automata and a New Liveness Algorithm
Venue: arXiv cs.FL 2025
Year: 2025

The translation of Metric Interval Temporal Logic (MITL) to timed automata is a topic that has been extensively studied. A key challenge here is the conversion of future modalities into equivalent automata. Typical conversions equip the automata with a guess-and-check mechanism to ascertain the truth of future modalities. Guess-and-check can be naturally implemented via alternation. However, since timed automata tools do not handle alternation, existing methods perform an additional step of converting the alternating timed automata into timed automata. This de-alternation step proceeds by an intricate finite abstraction of the space of configurations of the alternating automaton. Recently, a model of generalized timed automata (GTA) has been proposed. The model comes with several powerful additional features, and yet, the best known zone-based reachability algorithms for timed automata have been extended to the GTA model, with the same complexity for all the zone operations. We provide a new concise translation from MITL to GTA. In particular, for the timed until modality, our translation offers an exponential improvement w.r.t. the state-of-the-art. Thanks to this conversion, MITL model checking reduces to checking liveness for GTAs. However, no liveness algorithm is known for GTAs. Due to the presence of future clocks, there is no finite time-abstract bisimulation (region equivalence) for GTAs, whereas liveness algorithms for timed automata crucially rely on the presence of the finite region equivalence. As our second contribution, we provide a new zone-based algorithm for checking Buchi non-emptiness in GTAs, which circumvents this fundamental challenge.
Surpassing Cosine Similarity for Multidimensional Comparisons: Dimension Insensitive Euclidean Metric
Venue: arXiv cs.LG 2025
Year: 2025

Advances in computational power and hardware efficiency have enabled tackling increasingly complex, high-dimensional problems. While artificial intelligence (AI) achieves remarkable results, the interpretability of high-dimensional solutions remains challenging. A critical issue is the comparison of multidimensional quantities, essential in techniques like Principal Component Analysis. Metrics such as cosine similarity are often used, for example in the development of natural language processing algorithms or recommender systems. However, the interpretability of such metrics diminishes as dimensions increase. This paper analyzes the effects of dimensionality, revealing significant limitations of cosine similarity, particularly its dependency on the dimension of vectors, leading to biased and poorly interpretable outcomes. To address this, we introduce a Dimension Insensitive Euclidean Metric (DIEM) which demonstrates superior robustness and generalizability across dimensions. DIEM maintains consistent variability and eliminates the biases observed in traditional metrics, making it a reliable tool for high-dimensional comparisons. An example of the advantages of DIEM over cosine similarity is reported for a large language model application. This novel metric has the potential to replace cosine similarity, providing a more accurate and insightful method to analyze multidimensional data in fields ranging from neuromotor control to machine learning.
How more data can hurt: Instability and regularization in next-generation reservoir computing
Venue: arXiv cs.LG 2025
Year: 2025

It has been found recently that more data can, counter-intuitively, hurt the performance of deep neural networks. Here, we show that a more extreme version of the phenomenon occurs in data-driven models of dynamical systems. To elucidate the underlying mechanism, we focus on next-generation reservoir computing (NGRC) -- a popular framework for learning dynamics from data. We find that, despite learning a better representation of the flow map with more training data, NGRC can adopt an ill-conditioned ``integrator'' and lose stability. We link this data-induced instability to the auxiliary dimensions created by the delayed states in NGRC. Based on these findings, we propose simple strategies to mitigate the instability, either by increasing regularization strength in tandem with data size, or by carefully introducing noise during training. Our results highlight the importance of proper regularization in data-driven modeling of dynamical systems.
Confidence-based Estimators for Predictive Performance in Model Monitoring
Venue: arXiv cs.LG 2025
Year: 2025

After a machine learning model has been deployed into production, its predictive performance needs to be monitored. Ideally, such monitoring can be carried out by comparing the model's predictions against ground truth labels. For this to be possible, the ground truth labels must be available relatively soon after inference. However, there are many use cases where ground truth labels are available only after a significant delay, or in the worst case, not at all. In such cases, directly monitoring the model's predictive performance is impossible. Recently, novel methods for estimating the predictive performance of a model when ground truth is unavailable have been developed. Many of these methods leverage model confidence or other uncertainty estimates and are experimentally compared against a naive baseline method, namely Average Confidence (AC), which estimates model accuracy as the average of confidence scores for a given set of predictions. However, until now the theoretical properties of the AC method have not been properly explored. In this paper, we try to fill this gap by reviewing the AC method and show that under certain general assumptions, it is an unbiased and consistent estimator of model accuracy with many desirable properties. We also compare this baseline estimator against some more complex estimators empirically and show that in many cases the AC method is able to beat the others, although the comparative quality of the different estimators is heavily case-dependent.
SpiralShard: Highly Concurrent and Secure Blockchain Sharding via Linked Cross-shard Endorsement
Venue: arXiv cs.CR 2025
Year: 2025

Blockchain sharding improves the scalability of blockchain systems by partitioning the whole blockchain state, nodes, and transaction workloads into different shards. However, existing blockchain sharding systems generally suffer from a small number of shards, resulting in limited concurrency. The main reason is that existing sharding systems require large shard sizes to ensure security. To enhance the concurrency of blockchain sharding securely, we propose SpiralShard. The intuition is to allow the existence of some shards with a larger fraction of malicious nodes (i.e., corrupted shards), thus reducing shard sizes. SpiralShard can configure more and smaller shards for higher concurrency at the same network size. To ensure security with the existence of corrupted shards, we propose the Linked Cross-shard Endorsement (LCE) protocol. According to our LCE protocol, the blocks of each shard are sequentially verified and endorsed (via intra-shard consensus) by a group of shards before being finalized. As a result, a corrupted shard can eliminate forks with the help of the other shards. We implement SpiralShard based on Harmony and conduct extensive evaluations. Experimental results show that, compared with Harmony, SpiralShard achieves around 19x throughput gain under a large network size with 4,000+ nodes.
Mon CHÉRI: Mitigating Uninitialized Memory Access with Conditional Capabilities
Venue: arXiv cs.CR 2025
Year: 2025

Up to 10% of memory-safety vulnerabilities in languages like C and C++ stem from uninitialized variables. This work addresses the prevalence and lack of adequate software mitigations for uninitialized memory issues, proposing architectural protections in hardware. Capability-based addressing, such as the University of Cambridge's CHERI, mitigates many memory defects, including spatial and temporal safety violations at an architectural level. CHERI, however, does not handle undefined behavior from uninitialized variables. We extend the CHERI capability model to include "conditional capabilities", enabling memory-access policies based on prior operations. This allows enforcement of policies that satisfy memory-safety objectives such as "no reads to memory without at least one prior write" (Write-before-Read). We present our architecture extension, compiler support, and detailed evaluation of our approach on the QEMU full-system simulator and a modified FPGA-based CHERI-RISCV softcore. Our evaluation shows conditional capabilities are practical, with high detection accuracy while adding a small (~3.5%) overhead which is comparable to the cost of baseline CHERI capabilities.
Controlling diverse robots by inferring Jacobian fields with deep networks
Venue: arXiv cs.RO 2025
Year: 2025

Mirroring the complex structures and diverse functions of natural organisms is a long-standing challenge in robotics. Modern fabrication techniques have greatly expanded the feasible hardware, but using these systems requires control software to translate the desired motions into actuator commands. Conventional robots can easily be modeled as rigid links connected by joints, but it remains an open challenge to model and control biologically inspired robots that are often soft or made of several materials, lack sensing capabilities, and may change their material properties with use. Here, we introduce a method that uses deep neural networks to map a video stream of a robot to its visuomotor Jacobian field (the sensitivity of all 3D points to the robot's actuators). Our method enables the control of robots from only a single camera, makes no assumptions about the robots' materials, actuation, or sensing, and is trained without expert intervention by observing the execution of random commands. We demonstrate our method on a diverse set of robot manipulators that vary in actuation, materials, fabrication, and cost. Our approach achieves accurate closed-loop control and recovers the causal dynamic structure of each robot. Because it enables robot control using a generic camera as the only sensor, we anticipate that our work will broaden the design space of robotic systems and serve as a starting point for lowering the barrier to robotic automation.
Deep Inverse Design for High-Level Synthesis
Venue: arXiv cs.AR 2025
Year: 2025

High-level synthesis (HLS) has significantly advanced the automation of digital circuits design, yet the need for expertise and time in pragma tuning remains challenging. Existing solutions for the design space exploration (DSE) adopt either heuristic methods, lacking essential information for further optimization potential, or predictive models, missing sufficient generalization due to the time-consuming nature of HLS and the exponential growth of the design space. To address these challenges, we propose Deep Inverse Design for HLS (DID4HLS), a novel approach that integrates graph neural networks and generative models. DID4HLS iteratively optimizes hardware designs aimed at compute-intensive algorithms by learning conditional distributions of design features from post-HLS data. Compared to four state-of-the-art DSE baselines, our method achieved an average improvement of 42.8% on average distance to reference set (ADRS) compared to the best-performing baselines across six benchmarks, while demonstrating high robustness and efficiency. The code is available at https://github.com/PingChang818/DID4HLS.
AirSketch: Generative Motion to Sketch
Venue: arXiv cs.CV 2025
Year: 2025

Illustration is a fundamental mode of human expression and communication. Certain types of motion that accompany speech can provide this illustrative mode of communication. While Augmented and Virtual Reality technologies (AR/VR) have introduced tools for producing drawings with hand motions (air drawing), they typically require costly hardware and additional digital markers, thereby limiting their accessibility and portability. Furthermore, air drawing demands considerable skill to achieve aesthetic results. To address these challenges, we introduce the concept of AirSketch, aimed at generating faithful and visually coherent sketches directly from hand motions, eliminating the need for complicated headsets or markers. We devise a simple augmentation-based self-supervised training procedure, enabling a controllable image diffusion model to learn to translate from highly noisy hand tracking images to clean, aesthetically pleasing sketches, while preserving the essential visual cues from the original tracking data. We present two air drawing datasets to study this problem. Our findings demonstrate that beyond producing photo-realistic images from precise spatial inputs, controllable image diffusion can effectively produce a refined, clear sketch from a noisy input. Our work serves as an initial step towards marker-less air drawing and reveals distinct applications of controllable diffusion models to AirSketch and AR/VR in general.
Detect, Investigate, Judge and Determine: A Knowledge-guided Framework for Few-shot Fake News Detection
Venue: arXiv cs.CL 2025
Year: 2025

Few-Shot Fake News Detection (FS-FND) aims to distinguish inaccurate news from real ones in extremely low-resource scenarios. This task has garnered increased attention due to the widespread dissemination and harmful impact of fake news on social media. Large Language Models (LLMs) have demonstrated competitive performance with the help of their rich prior knowledge and excellent in-context learning abilities. However, existing methods face significant limitations, such as the Understanding Ambiguity and Information Scarcity, which significantly undermine the potential of LLMs. To address these shortcomings, we propose a Dual-perspective Knowledge-guided Fake News Detection (DKFND) model, designed to enhance LLMs from both inside and outside perspectives. Specifically, DKFND first identifies the knowledge concepts of each news article through a Detection Module. Subsequently, DKFND creatively designs an Investigation Module to retrieve inside and outside valuable information concerning to the current news, followed by another Judge Module to evaluate the relevance and confidence of them. Finally, a Determination Module further derives two respective predictions and obtain the final result. Extensive experiments on two public datasets show the efficacy of our proposed method, particularly in low-resource settings.
PriRoAgg: Achieving Robust Model Aggregation with Minimum Privacy Leakage for Federated Learning
Venue: arXiv cs.CR 2025
Year: 2025

Federated learning (FL) has recently gained significant momentum due to its potential to leverage large-scale distributed user data while preserving user privacy. However, the typical paradigm of FL faces challenges of both privacy and robustness: the transmitted model updates can potentially leak sensitive user information, and the lack of central control of the local training process leaves the global model susceptible to malicious manipulations on model updates. Current solutions attempting to address both problems under the one-server FL setting fall short in the following aspects: 1) designed for simple validity checks that are insufficient against advanced attacks (e.g., checking norm of individual update); and 2) partial privacy leakage for more complicated robust aggregation algorithms (e.g., distances between model updates are leaked for multi-Krum). In this work, we formalize a novel security notion of aggregated privacy that characterizes the minimum amount of user information, in the form of some aggregated statistics of users' updates, that is necessary to be revealed to accomplish more advanced robust aggregation. We develop a general framework PriRoAgg, utilizing Lagrange coded computing and distributed zero-knowledge proof, to execute a wide range of robust aggregation algorithms while satisfying aggregated privacy. As concrete instantiations of PriRoAgg, we construct two secure and robust protocols based on state-of-the-art robust algorithms, for which we provide full theoretical analyses on security and complexity. Extensive experiments are conducted for these protocols, demonstrating their robustness against various model integrity attacks, and their efficiency advantages over baselines.
Robustness of Large Language Models to Perturbations in Text
Venue: arXiv cs.CL 2025
Year: 2025

Having a clean dataset has been the foundational assumption of most natural language processing (NLP) systems. However, properly written text is rarely found in real-world scenarios and hence, oftentimes invalidates the aforementioned foundational assumption. Recently, Large language models (LLMs) have shown impressive performance, but can they handle the inevitable noise in real-world data? This work tackles this critical question by investigating LLMs' resilience against morphological variations in text. To that end, we artificially introduce varying levels of noise into a diverse set of datasets and systematically evaluate LLMs' robustness against the corrupt variations of the original text. Our findings show that contrary to popular beliefs, generative LLMs are quiet robust to noisy perturbations in text. This is a departure from pre-trained models like BERT or RoBERTa whose performance has been shown to be sensitive to deteriorating noisy text. Additionally, we test LLMs' resilience on multiple real-world benchmarks that closely mimic commonly found errors in the wild. With minimal prompting, LLMs achieve a new state-of-the-art on the benchmark tasks of Grammar Error Correction (GEC) and Lexical Semantic Change (LSC). To empower future research, we also release a dataset annotated by humans stating their preference for LLM vs. human-corrected outputs along with the code to reproduce our results.
Systematic Categorization, Construction and Evaluation of New Attacks against Multi-modal Mobile GUI Agents
Venue: arXiv cs.CR 2025
Year: 2025

The integration of Large Language Models (LLMs) and Multi-modal Large Language Models (MLLMs) into mobile GUI agents has significantly enhanced user efficiency and experience. However, this advancement also introduces potential security vulnerabilities that have yet to be thoroughly explored. In this paper, we present a systematic security investigation of multi-modal mobile GUI agents, addressing this critical gap in the existing literature. Our contributions are twofold: (1) we propose a novel threat modeling methodology, leading to the discovery and feasibility analysis of 34 previously unreported attacks, and (2) we design an attack framework to systematically construct and evaluate these threats. Through a combination of real-world case studies and extensive dataset-driven experiments, we validate the severity and practicality of those attacks, highlighting the pressing need for robust security measures in mobile GUI systems.